A technical breakdown of Model Cascade architecture, which routes queries between cheaper proxy models and expensive oracle models based on confidence scores. The strategy can save engineering teams around $100K annually by handling simple classification tasks with smaller models and escalating only uncertain queries to larger ones.
Model Cascade architecture, which is an advanced strategy for optimizing the cost accuracy trade off for your LLM applications. How do they work? They work by routing queries from a cheap proxy model to an expensive Oracle model based on a confidence score. Log probabilities are the most common way to measure confidence. This technique works really well for classification tasks, but not so well for others. Everything else? Consider it experimental. You need a well labeled data set to tune the confidence thresholds to meet your specific bar for your LLM applications in your industry and in your domain. So again, look at the data and see do I have classification tasks where I could route this to a cheap proxy model and fine tune my costs.
